Register Generation Model

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations indicate idempotentHint=true, which is helpful, and the description adds that this 'creates a generation model (Model Under Test)' pointing to a specific LLM. It clarifies the registration semantics (registering creates an instance) and the relationship to testing, which goes beyond the annotations. It doesn't mention persistence or side effects, but the idempotentHint covers re-registration safety.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and well-structured. It front-loads the core purpose, provides usage guidance, and ends with parameter explanations. Every sentence earns its place, and the formatting with Args clearly separates parameter descriptions.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity, the description fully covers what an agent needs: the purpose, the required parameters, how to discover valid model_name values, and how the result is used. The presence of an output schema means the description doesn't need to explain return values. The sibling context (list_available_llms, run_test) is addressed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so the description carries the full burden. It explains 'name' as 'a human-readable name' and 'model_name' as 'the LLM from the registry (from list_available_llms)', which adds meaning beyond the raw schema types. It could provide more detail on name uniqueness, but the description effectively maps parameters to their semantics.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Register a generation model for testing by selecting an LLM from the registry.' It uses specific verbs ('register', 'creates') and identifies the resource ('generation model') and distinguishes it from sibling tools like list_generation_models or update_generation_model. The example LLM ('azure/gpt-4o-mini') further clarifies the intended use.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly tells users to use 'list_available_llms' to see available LLMs and mentions that the registered model can be used with 'run_test'. This provides clear when-to-use context and names alternatives/prerequisites, which is excellent guidance for an AI agent.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.